from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

Time report

print_time_report()

	hour	min	sec
algo
KNeighborsClassifier	0.0	35.0	59.740371
daal4py_KNeighborsClassifier	0.0	5.0	24.088212
KNeighborsClassifier_kd_tree	0.0	2.0	42.730927
daal4py_KNeighborsClassifier_kd_tree	0.0	0.0	32.134958
KMeans_tall	0.0	0.0	24.719242
daal4py_KMeans_tall	0.0	0.0	9.925425
KMeans_short	0.0	0.0	3.569772
daal4py_KMeans_short	0.0	0.0	1.859428
LogisticRegression	0.0	0.0	25.032409
daal4py_LogisticRegression	0.0	0.0	5.096089
Ridge	0.0	0.0	11.242000
daal4py_Ridge	0.0	0.0	2.209464
HistGradientBoostingClassifier	0.0	5.0	6.229003
lightgbm	0.0	5.0	26.358922
xgboost	0.0	5.0	46.914450
catboost	0.0	5.0	18.174028
total	1.0	7.0	40.130145

Environment information

print_env_info()

{
  "system_info": {
    "python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.2",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.20.3",
    "scipy": "1.6.3",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": "3.4.2",
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}

scikit-learn's estimators vs daal4py

reporting = Reporting(config_file_path="config.yml")
reporting.run()

KNeighborsClassifier vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	brute

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.172	0.000	4.656	0.000	1	100	NaN	NaN	0.486	0.000	0.354	0.000	See	See
1	KNeighborsClassifier	predict	1000000	1000	100	NaN	24.624	0.268	0.000	0.025	1	100	0.938	0.800	3.858	0.045	6.382	0.102	See	See
2	KNeighborsClassifier	predict	1000000	1	100	NaN	0.201	0.004	0.000	0.201	1	100	1.000	1.000	0.101	0.002	1.985	0.061	See	See
3	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.125	0.000	6.382	0.000	-1	100	NaN	NaN	0.479	0.000	0.261	0.000	See	See
4	KNeighborsClassifier	predict	1000000	1000	100	NaN	37.608	0.000	0.000	0.038	-1	100	0.938	0.800	3.825	0.018	9.832	0.047	See	See
5	KNeighborsClassifier	predict	1000000	1	100	NaN	0.197	0.012	0.000	0.197	-1	100	1.000	1.000	0.102	0.002	1.941	0.118	See	See
6	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.124	0.000	6.433	0.000	-1	5	NaN	NaN	0.483	0.000	0.257	0.000	See	See
7	KNeighborsClassifier	predict	1000000	1000	100	NaN	37.594	0.000	0.000	0.038	-1	5	0.818	0.937	3.903	0.029	9.633	0.071	See	See
8	KNeighborsClassifier	predict	1000000	1	100	NaN	0.197	0.015	0.000	0.197	-1	5	1.000	1.000	0.102	0.001	1.933	0.151	See	See
9	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.131	0.000	6.119	0.000	-1	1	NaN	NaN	0.469	0.000	0.279	0.000	See	See
10	KNeighborsClassifier	predict	1000000	1000	100	NaN	28.683	0.301	0.000	0.029	-1	1	0.720	0.711	3.823	0.026	7.503	0.093	See	See
11	KNeighborsClassifier	predict	1000000	1	100	NaN	0.181	0.016	0.000	0.181	-1	1	0.000	1.000	0.100	0.001	1.807	0.161	See	See
12	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.121	0.000	6.637	0.000	1	1	NaN	NaN	0.467	0.000	0.258	0.000	See	See
13	KNeighborsClassifier	predict	1000000	1000	100	NaN	15.540	0.366	0.000	0.016	1	1	0.720	0.937	3.925	0.035	3.959	0.100	See	See
14	KNeighborsClassifier	predict	1000000	1	100	NaN	0.191	0.003	0.000	0.191	1	1	0.000	1.000	0.102	0.002	1.872	0.052	See	See
15	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.130	0.000	6.174	0.000	1	5	NaN	NaN	0.464	0.000	0.279	0.000	See	See
16	KNeighborsClassifier	predict	1000000	1000	100	NaN	24.398	0.298	0.000	0.024	1	5	0.818	0.711	3.805	0.022	6.412	0.087	See	See
17	KNeighborsClassifier	predict	1000000	1	100	NaN	0.201	0.005	0.000	0.201	1	5	1.000	1.000	0.104	0.004	1.933	0.090	See	See
18	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.054	0.000	0.298	0.000	1	100	NaN	NaN	0.100	0.000	0.536	0.000	See	See
19	KNeighborsClassifier	predict	1000000	1000	2	NaN	19.770	0.142	0.000	0.020	1	100	0.980	0.985	0.833	0.019	23.748	0.557	See	See
20	KNeighborsClassifier	predict	1000000	1	2	NaN	0.024	0.002	0.000	0.024	1	100	1.000	1.000	0.005	0.001	5.163	1.023	See	See
21	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.057	0.000	0.279	0.000	-1	100	NaN	NaN	0.106	0.000	0.540	0.000	See	See
22	KNeighborsClassifier	predict	1000000	1000	2	NaN	32.532	0.000	0.000	0.033	-1	100	0.980	0.985	0.825	0.017	39.428	0.824	See	See
23	KNeighborsClassifier	predict	1000000	1	2	NaN	0.028	0.003	0.000	0.028	-1	100	1.000	1.000	0.005	0.001	5.870	1.428	See	See
24	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.054	0.000	0.296	0.000	-1	5	NaN	NaN	0.104	0.000	0.518	0.000	See	See
25	KNeighborsClassifier	predict	1000000	1000	2	NaN	32.521	0.000	0.000	0.033	-1	5	0.979	0.985	0.900	0.020	36.152	0.817	See	See
26	KNeighborsClassifier	predict	1000000	1	2	NaN	0.029	0.002	0.000	0.029	-1	5	1.000	1.000	0.004	0.000	6.724	0.690	See	See
27	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.058	0.000	0.274	0.000	-1	1	NaN	NaN	0.095	0.000	0.613	0.000	See	See
28	KNeighborsClassifier	predict	1000000	1000	2	NaN	23.625	0.127	0.000	0.024	-1	1	0.963	0.976	0.815	0.018	28.988	0.661	See	See
29	KNeighborsClassifier	predict	1000000	1	2	NaN	0.020	0.001	0.000	0.020	-1	1	1.000	1.000	0.004	0.000	4.620	0.431	See	See
30	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.056	0.000	0.285	0.000	1	1	NaN	NaN	0.101	0.000	0.555	0.000	See	See
31	KNeighborsClassifier	predict	1000000	1000	2	NaN	10.653	0.082	0.000	0.011	1	1	0.963	0.985	0.885	0.020	12.038	0.287	See	See
32	KNeighborsClassifier	predict	1000000	1	2	NaN	0.015	0.001	0.000	0.015	1	1	1.000	1.000	0.005	0.001	3.038	0.399	See	See
33	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.057	0.000	0.282	0.000	1	5	NaN	NaN	0.099	0.000	0.575	0.000	See	See
34	KNeighborsClassifier	predict	1000000	1000	2	NaN	19.870	0.352	0.000	0.020	1	5	0.979	0.976	0.821	0.013	24.193	0.583	See	See
35	KNeighborsClassifier	predict	1000000	1	2	NaN	0.023	0.001	0.000	0.023	1	5	1.000	1.000	0.004	0.000	5.255	0.422	See	See

KNeighborsClassifier_kd_tree vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	kd_tree

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.190	0.000	0.025	0.000	1	1	NaN	NaN	0.791	0.000	4.033	0.000	See	See
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.743	0.019	0.000	0.001	1	1	0.963	0.967	0.121	0.007	6.140	0.410	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	2.685	1.216	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.906	0.000	0.028	0.000	-1	1	NaN	NaN	0.707	0.000	4.113	0.000	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.433	0.006	0.000	0.000	-1	1	0.963	0.977	0.221	0.008	1.963	0.073	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.004	0.002	0.000	0.004	-1	1	1.000	1.000	0.001	0.000	7.560	5.148	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.908	0.000	0.028	0.000	1	100	NaN	NaN	0.711	0.000	4.088	0.000	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	4.973	0.134	0.000	0.005	1	100	0.979	0.978	0.697	0.067	7.133	0.711	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.001	0.000	0.003	1	100	1.000	1.000	0.001	0.000	2.377	0.873	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.145	0.000	0.025	0.000	-1	5	NaN	NaN	0.734	0.000	4.283	0.000	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.857	0.015	0.000	0.001	-1	5	0.972	0.978	0.667	0.014	1.286	0.034	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.000	0.000	0.003	-1	5	1.000	1.000	0.001	0.000	2.703	0.983	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.021	0.000	0.026	0.000	-1	100	NaN	NaN	0.712	0.000	4.244	0.000	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	2.780	0.036	0.000	0.003	-1	100	0.979	0.977	0.218	0.006	12.725	0.378	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.005	0.001	0.000	0.005	-1	100	1.000	1.000	0.001	0.000	9.976	3.649	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.870	0.000	0.028	0.000	1	5	NaN	NaN	0.693	0.000	4.139	0.000	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	1.443	0.022	0.000	0.001	1	5	0.972	0.967	0.114	0.003	12.629	0.345	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	2.679	1.067	See	See
18	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.752	0.000	0.021	0.000	1	1	NaN	NaN	0.489	0.000	1.538	0.000	See	See
19	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.033	0.001	0.000	0.000	1	1	0.968	0.986	0.001	0.000	40.940	19.078	See	See
20	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	5.002	2.585	See	See
21	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.829	0.000	0.019	0.000	-1	1	NaN	NaN	0.506	0.000	1.637	0.000	See	See
22	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.037	0.005	0.000	0.000	-1	1	0.968	0.991	0.001	0.000	30.462	7.897	See	See
23	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.003	0.000	0.000	0.003	-1	1	1.000	1.000	0.000	0.000	14.880	8.261	See	See
24	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.760	0.000	0.021	0.000	1	100	NaN	NaN	0.481	0.000	1.581	0.000	See	See
25	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.062	0.002	0.000	0.000	1	100	0.984	0.988	0.009	0.003	7.251	2.233	See	See
26	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	100	1.000	1.000	0.000	0.000	5.316	2.361	See	See
27	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.797	0.000	0.020	0.000	-1	5	NaN	NaN	0.480	0.000	1.660	0.000	See	See
28	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.036	0.001	0.000	0.000	-1	5	0.980	0.988	0.007	0.001	5.037	0.635	See	See
29	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.003	0.001	0.000	0.003	-1	5	1.000	1.000	0.000	0.000	17.438	7.653	See	See
30	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.776	0.000	0.021	0.000	-1	100	NaN	NaN	0.504	0.000	1.539	0.000	See	See
31	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.057	0.002	0.000	0.000	-1	100	0.984	0.991	0.001	0.000	51.379	12.245	See	See
32	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.004	0.001	0.000	0.004	-1	100	1.000	1.000	0.000	0.000	24.240	12.751	See	See
33	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.798	0.000	0.020	0.000	1	5	NaN	NaN	0.506	0.000	1.576	0.000	See	See
34	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.037	0.002	0.000	0.000	1	5	0.980	0.986	0.001	0.002	28.141	39.221	See	See
35	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	4.827	2.264	See	See

KMeans_tall vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	3
max_iter	30
n_init	1
tol	0.0

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_tall	fit	1000000	1000000	2	30	0.683	0.0	0.702	0.000	k-means++	NaN	30	NaN	0.314	0.0	2.174	0.000	See	See
1	KMeans_tall	predict	1000000	1000	2	30	0.002	0.0	0.273	0.000	k-means++	0.001	30	0.001	0.000	0.0	8.674	3.266	See	See
2	KMeans_tall	predict	1000000	1	2	30	0.001	0.0	0.000	0.001	k-means++	1.000	30	1.000	0.000	0.0	8.803	3.810	See	See
3	KMeans_tall	fit	1000000	1000000	2	30	0.603	0.0	0.796	0.000	random	NaN	30	NaN	0.271	0.0	2.227	0.000	See	See
4	KMeans_tall	predict	1000000	1000	2	30	0.002	0.0	0.317	0.000	random	0.001	30	0.001	0.000	0.0	6.751	2.837	See	See
5	KMeans_tall	predict	1000000	1	2	30	0.001	0.0	0.000	0.001	random	1.000	30	1.000	0.000	0.0	8.900	3.743	See	See
6	KMeans_tall	fit	1000000	1000000	100	30	7.314	0.0	3.281	0.000	k-means++	NaN	30	NaN	3.872	0.0	1.889	0.000	See	See
7	KMeans_tall	predict	1000000	1000	100	30	0.002	0.0	13.425	0.000	k-means++	0.002	30	0.002	0.000	0.0	5.968	1.843	See	See
8	KMeans_tall	predict	1000000	1	100	30	0.002	0.0	0.016	0.002	k-means++	1.000	30	1.000	0.000	0.0	6.133	5.768	See	See
9	KMeans_tall	fit	1000000	1000000	100	30	6.536	0.0	3.672	0.000	random	NaN	30	NaN	3.701	0.0	1.766	0.000	See	See
10	KMeans_tall	predict	1000000	1000	100	30	0.002	0.0	13.763	0.000	random	0.002	30	0.002	0.000	0.0	6.021	2.099	See	See
11	KMeans_tall	predict	1000000	1	100	30	0.002	0.0	0.016	0.002	random	1.000	30	1.000	0.000	0.0	8.678	4.110	See	See

KMeans_short vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	300
max_iter	20
n_init	1
tol	0.0

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_short	fit	10000	10000	2	20	0.301	0.000	0.011	0.000	k-means++	NaN	20	NaN	0.051	0.0	5.895	0.000	See	See
1	KMeans_short	predict	10000	1000	2	20	0.002	0.000	0.160	0.000	k-means++	-0.000	20	0.001	0.001	0.0	3.304	0.545	See	See
2	KMeans_short	predict	10000	1	2	20	0.002	0.000	0.000	0.002	k-means++	1.000	20	1.000	0.000	0.0	9.136	4.091	See	See
3	KMeans_short	fit	10000	10000	2	20	0.093	0.000	0.034	0.000	random	NaN	20	NaN	0.133	0.0	0.697	0.000	See	See
4	KMeans_short	predict	10000	1000	2	20	0.002	0.000	0.166	0.000	random	0.003	20	0.000	0.001	0.0	3.198	0.589	See	See
5	KMeans_short	predict	10000	1	2	20	0.002	0.001	0.000	0.002	random	1.000	20	1.000	0.000	0.0	8.680	5.473	See	See
6	KMeans_short	fit	10000	10000	100	20	1.064	0.000	0.150	0.000	k-means++	NaN	20	NaN	0.252	0.0	4.222	0.000	See	See
7	KMeans_short	predict	10000	1000	100	20	0.003	0.000	5.393	0.000	k-means++	0.260	20	0.364	0.001	0.0	2.070	0.266	See	See
8	KMeans_short	predict	10000	1	100	20	0.002	0.000	0.010	0.002	k-means++	1.000	20	1.000	0.000	0.0	7.205	3.067	See	See
9	KMeans_short	fit	10000	10000	100	20	0.318	0.000	0.503	0.000	random	NaN	20	NaN	0.607	0.0	0.524	0.000	See	See
10	KMeans_short	predict	10000	1000	100	20	0.003	0.000	5.557	0.000	random	0.293	20	0.290	0.001	0.0	2.088	0.213	See	See
11	KMeans_short	predict	10000	1	100	20	0.002	0.000	0.010	0.002	random	1.000	20	1.000	0.000	0.0	6.815	4.722	See	See

LogisticRegression vs daal4py

All estimators share the following hyperparameters:

	value
penalty	l2
dual	False
tol	0.0001
C	1.0
fit_intercept	True
intercept_scaling	1
class_weight	NaN
random_state	NaN
solver	lbfgs
max_iter	100
multi_class	auto
verbose	0
warm_start	False
n_jobs	NaN
l1_ratio	NaN

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	LogisticRegression	fit	1000000	1000000	100	[20]	15.358	0.0	[-0.07682523]	0.000	NaN	NaN	NaN	NaN	NaN	2.709	0.000	5.670	0.000	See	See
1	LogisticRegression	predict	1000000	1000	100	[20]	0.000	0.0	[48.5965174]	0.000	NaN	NaN	NaN	NaN	0.543	0.000	0.000	0.844	0.336	See	See
2	LogisticRegression	predict	1000000	1	100	[20]	0.000	0.0	[0.18079014]	0.000	NaN	NaN	NaN	NaN	0.000	0.000	0.000	0.353	0.230	See	See
3	LogisticRegression	fit	1000	1000	10000	[28]	1.319	0.0	[-1.55839]	0.001	NaN	NaN	NaN	NaN	NaN	0.920	0.000	1.434	0.000	See	See
4	LogisticRegression	predict	1000	100	10000	[28]	0.002	0.0	[99.47988452]	0.000	NaN	NaN	NaN	NaN	0.230	0.004	0.001	0.539	0.080	See	See
5	LogisticRegression	predict	1000	1	10000	[28]	0.000	0.0	[18.43005547]	0.000	NaN	NaN	NaN	NaN	0.000	0.001	0.000	0.148	0.071	See	See

Ridge vs daal4py

All estimators share the following hyperparameters:

	value
alpha	1.0
fit_intercept	True
normalize	deprecated
copy_X	True
max_iter	NaN
tol	0.001
solver	auto
random_state	NaN

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	Ridge	fit	1000	1000	10000	NaN	0.293	0.000	0.273	0.0	NaN	NaN	NaN	0.288	0.0	1.018	0.000	See	See
1	Ridge	predict	1000	1000	10000	NaN	0.012	0.001	6.845	0.0	NaN	NaN	0.094	0.018	0.0	0.649	0.072	See	See
2	Ridge	predict	1000	1	10000	NaN	0.000	0.000	0.790	0.0	NaN	NaN	NaN	0.000	0.0	0.615	0.396	See	See
3	Ridge	fit	1000000	1000000	100	NaN	1.388	0.000	0.576	0.0	NaN	NaN	NaN	0.342	0.0	4.054	0.000	See	See
4	Ridge	predict	1000000	1000	100	NaN	0.000	0.000	4.362	0.0	NaN	NaN	1.000	0.000	0.0	0.715	0.335	See	See
5	Ridge	predict	1000000	1	100	NaN	0.000	0.000	0.009	0.0	NaN	NaN	NaN	0.000	0.0	0.685	0.426	See	See

Randomized HPO Benchmarks

reporting_hpo = ReportingHpo(files=[
    "results/benchmarking/sklearn_HistGradientBoostingClassifier.csv",
    "results/benchmarking/xgboost_XGBClassifier.csv",
    "results/benchmarking/lightgbm_LGBMClassifier.csv",
    "results/benchmarking/catboost_CatBoostClassifier.csv"
])
reporting_hpo.run()

Scatter

Permutated curves

25th percentile

Mean

75th percentile